期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 47, 期 9, 页码 2824-2837出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2586191
关键词
Angle-based-selection (ABS); decompositionbased-sorting (DBS); diversity; evolutionary multiobjective optimization; many-objective optimization
类别
资金
- National Natural Science Foundation of China [61300159, 61473241, 61332002, 61370185, 61175073]
- Natural Science Foundation of Jiangsu Province of China [BK20130808]
- China Post-Doctoral Science Foundation [2015M571751]
- Science and Technology Planning Project of Guangdong Province of China [2013B011304002]
- Educational Commission of Guangdong Province of China [2015KGJHZ014]
- Fundamental Research Funds for the Central Universities of China [NZ2013306]
- Guangdong High-Level University Project Green Technologies for Marine Industries
Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into a number of scalar optimization subproblems and then solves them in parallel. In many MOEA/D variants, each subproblem is associated with one and only one solution. An underlying assumption is that each subproblem has a different Pareto-optimal solution, which may not be held, for irregular Pareto fronts (PFs), e.g., disconnected and degenerate ones. In this paper, we propose a new variant of MOEA/D with sorting-and-selection (MOEA/D-SAS). Different from other selection schemes, the balance between convergence and diversity is achieved by two distinctive components, decomposition-basedsorting (DBS) and angle-based-selection (ABS). DBS only sorts L closest solutions to each subproblem to control the convergence and reduce the computational cost. The parameter L has been made adaptive based on the evolutionary process. ABS takes use of angle information between solutions in the objective space to maintain a more fine-grained diversity. In MOEA/D-SAS, different solutions can be associated with the same subproblems; and some subproblems are allowed to have no associated solution, more flexible to MOPs or many-objective optimization problems (MaOPs) with different shapes of PFs. Comprehensive experimental studies have shown that MOEA/D-SAS outperforms other approaches; and is especially effective on MOPs or MaOPs with irregular PFs. Moreover, the computational efficiency of DBS and the effects of ABS in MOEA/D-SAS are also investigated and discussed in detail.
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